BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-20723@www.clsp.jhu.edu DTSTAMP:20240328T120159Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon.\n Biography\n\nJessy Li is an assistant professor in the Department of Lingu istics at UT Austin where she works on in computational linguistics and na tural language processing. Her work focuses on discourse processing\, text generation\, and language pragmatics in social media. She received her Ph .D. in 2017 from the University of Pennsylvania. She received an ACM SIGSO FT Distinguished Paper Award at FSE 2019\, an Area Chair Favorite at COLIN G 2018\, and a Best Paper nomination at SIGDIAL 2016.\nWeb: https://jessyl i.com DTSTART;TZID=America/New_York:20210917T120000 DTEND;TZID=America/New_York:20210917T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Jessy Li (University of Texas at Austin – Virtual Visit) “New Chall enges in Text Simplification” URL:https://www.clsp.jhu.edu/events/jessy-li-university-of-texas-at-austin/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon. p>\n
Biography
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\nI will present our work on data a ugmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data\, and the task is named entity recognition (NER). The premise is that we can transfo rm the labelled out of domain data into something that stylistically is mo re closely related to the target data. Then we can train a model on a comb ination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results o n these efforts.
\nIf time allows\, I will also give an overview of other research projects I’m currently leading at RiTUA L (Research in Text Understanding and Analysis of Language) lab. The commo n thread among all these research problems is the scarcity of labeled data .
\nBiography
\nThamar Solorio is a Professor of Computer Science at the Univer sity of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica\, Óptica y Electrónica\, in Puebla\, Mexico. Her research interests include information extraction from social media data\, enabling technology for code-switched data\, stylistic model ing of text\, and more recently multimodal approaches for online content u nderstanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attrib ution\, and recipient of the 2014 Emerging Leader ABIE Award in Honor of D enice Denton. She is currently serving a second term as an elected board m ember of the North American Chapter of the Association of Computational Li nguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.
\n X-TAGS;LANGUAGE=en-US:2022\,September\,Solorio END:VEVENT BEGIN:VEVENT UID:ai1ec-23882@www.clsp.jhu.edu DTSTAMP:20240328T120159Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.\nBiograph y\nLei Li is an Assistant Professor in Language Technology Institute at Ca rnegie Mellon University. He received Ph.D. from Carnegie Mellon Universit y School of Computer Science. He is a recipient of ACL 2021 Best Paper Awa rd\, CCF Young Elite Award in 2019\, CCF distinguished speaker in 2017\, W u Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runn er-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\ , he was a faculty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI Lab in 2016 and led its research in NLP\, ML\, Robotics\, an d Drug Discovery. He launched ByteDance’s machine translation system VolcT rans and AI writing system Xiaomingbot\, serving one billion users. DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.
\n< strong>Biography
\nLei Li is an Assistant Professor in Lang uage Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recip ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m achine translation system VolcTrans and AI writing system Xiaomingbot\, se rving one billion users.
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